Chiang Mai Journal of Science

An Agent Model for Information Filtering using Revolutionary RSVD Technique

Paper Type	Contributed Paper
Title	An Agent Model for Information Filtering using Revolutionary RSVD Technique
Author	Dussadee Praserttitipong [a] * and Peraphon Sophatsathit [b]
Email	speraphon@gmail.com; dussadee.p@cmu.ac.th
Abstract:  This paper proposes a collaborative software agent model.  The agent works in a distributed environment making recommendation based on its up-to-date knowledge.  This knowledge is partly acquired from other collaborative agents to combine with its own prior knowledge by means of a revolutionary regularized singular value decomposition (rRSVD) technique.  The technique is used as an adaptation process for the agent to learn and update the knowledge periodically.  This process employs one of the three agent adaptation models, namely, 2-phase, 1-phase, or non-adaptation that is suitable for the operating bandwidth, along with a fast incremental knowledge adaptation algorithm. As a consequence, the adapted agent will be able to work alone in a distributed environment at a satisfactorily level of performance.
Start & End Page	1429 - 1438
Received Date	2012-03-20
Revised Date
Accepted Date	2013-07-11
Full Text	Download
Keyword	recommender systems, collaborative agent, adaptation process, distributed environment
Volume	Vol.41 No.5/2 (OCTOBER 2014)
DOI
Citation	Praserttitipong D. and Sophatsathit P., An Agent Model for Information Filtering using Revolutionary RSVD Technique , Chiang Mai J. Sci., 2014; 41(5/2): 1429-1438.
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